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Adaptive power priors with empirical Bayes for clinical trials


Gravestock, Isaac; Held, Leonhard (2017). Adaptive power priors with empirical Bayes for clinical trials. Pharmaceutical Statistics, 16(5):349-360.

Abstract

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much discussion as a way to increase the feasibility of trials in situations where patients are difficult to recruit. The best method to include this data is not yet clear, especially in the case when few historical studies are available. This paper looks at the power prior technique afresh in a binomial setting and examines some previously unexamined properties, such as Box P values, bias, and coverage. Additionally, it proposes an empirical Bayes-type approach to estimating the prior weight parameter by marginal likelihood. This estimate has advantages over previously criticised methods in that it varies commensurably with differences in the historical and current data and can choose weights near 1 when the data are similar enough. Fully Bayesian approaches are also considered. An analysis of the operating characteristics shows that the adaptive methods work well and that the various approaches have different strengths and weaknesses.

Abstract

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much discussion as a way to increase the feasibility of trials in situations where patients are difficult to recruit. The best method to include this data is not yet clear, especially in the case when few historical studies are available. This paper looks at the power prior technique afresh in a binomial setting and examines some previously unexamined properties, such as Box P values, bias, and coverage. Additionally, it proposes an empirical Bayes-type approach to estimating the prior weight parameter by marginal likelihood. This estimate has advantages over previously criticised methods in that it varies commensurably with differences in the historical and current data and can choose weights near 1 when the data are similar enough. Fully Bayesian approaches are also considered. An analysis of the operating characteristics shows that the adaptive methods work well and that the various approaches have different strengths and weaknesses.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Statistics and Probability, Pharmacology (medical), Pharmacology
Language:English
Date:2017
Deposited On:30 Jan 2018 21:47
Last Modified:19 Aug 2018 13:19
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:1539-1604
Additional Information:For accepted manuscripts: This is the peer reviewed version of the following article: Gravestock I, Held L, On behalf of the COMBACTE-Net consortium. Adaptive power priors with empirical Bayes for clinical trials. Pharmaceutical Statistics. 2017;16:349–360., which has been published in final form at https://doi.org/10.1002/pst.1814. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms).
OA Status:Closed
Publisher DOI:https://doi.org/10.1002/pst.1814
PubMed ID:28574202

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Embargo till: 2018-10-01